Preparation of Ni/CuO/MCM-41 for indole oxidation: optimisation processes
Bibliographic record
Abstract
In this study, several catalysts – namely, MCM-41, copper (II) oxide (CuO), copper (II) oxide/MCM-41, nickel (Ni)/copper (II) oxide and nickel/copper (II) oxide/MCM-41 – were synthesised and characterised by way of X-ray diffraction, Fourier transform infrared spectroscopy, field-emission scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy and Brunauer–Emmett–Teller/Barrett–Joyner–Halenda techniques. The oxidation of indole (C 8 H 7 N) by these synthesised catalysts was evaluated at room temperature by using an ultraviolet spectrophotometer. It was revealed that adding nickel to copper (II) oxide/MCM-41, even at low concentrations, significantly increased the oxidation efficiency. Furthermore, to obtain the optimal operating conditions, the influences of the weight percentage of nickel, pH, the mass of catalyst and contact time, each at three levels, on indole oxidation were studied by applying the response surface methodology based on the Box–Behnken design method. The obtained results indicated that pH and nickel weight percentage were the most critical factors. Finally, although several kinetic models were applied for investigating the kinetic mechanism of indole oxidation by way of the nickel/copper (II) oxide/MCM-41 composite, the most suitable model for this purpose was the Blanchard kinetic model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".